The field of system identification and control is witnessing significant advancements, driven by the adoption of active learning strategies, data-driven approaches, and innovative applications of existing techniques. Researchers are exploring new methods to improve the efficiency and accuracy of system identification, such as online design of experiments and coreset selection, which enable more effective data collection and analysis. The integration of data-driven modeling with traditional model-based control is also gaining traction, as seen in the development of prescribed-time control frameworks that combine the strengths of both approaches. Furthermore, the use of machine learning and deep learning techniques, such as generative latent diffusion and parametric neural amp modeling, is opening up new avenues for efficient spatiotemporal data reduction and virtual amp modeling. Notable papers in this area include the work on Thompson Sampling-Based Learning and Control for Unknown Dynamic Systems, which proposes a novel approach to active learning-based controller design, and the paper on A Data-Driven Prescribed-Time Control Framework via Koopman Operator and Adaptive Backstepping, which presents a synergy of data-driven modeling and model-based control.